
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

Data_science_information = pd.read_csv("DataScienceSalaries_8b290669-a5e9-45bf-be72-d27add2eacae_93472_.csv")#reading data from a csv file
Data_Science_DataFrame = pd.DataFrame(Data_science_information)# keeping the imported datas in a dataframe.

pd.set_option('display.max_columns',None) #To display all the columns in the dataframe
pd.set_option('display.max_rows',None) #To display all the rows in the dataframe

Data_Science_DataFrame


Data_Science_DataFrame = Data_Science_DataFrame.drop(['salary','salary_currency'], axis = 1)

Data_Science_DataFrame


# First identifying if the NaN values exists or not

NaN_values = Data_Science_DataFrame.isna().sum() #Here the sum() counts the number of missing values in each coumn
print("The NaN values in the dataframe are :")
NaN_values

The NaN values in the dataframe are :

work_year             0
experience_level      0
employment_type       0
job_title             0
salary_in_usd         0
employee_residence    0
remote_ratio          0
company_location      0
company_size          0
dtype: int64


#Now to remove the NaN values

Remove_NaN_values = Data_Science_DataFrame.dropna()
print("The dataframe after removing the NaN values are : ")

Remove_NaN_values

The dataframe after removing the NaN values are :


#Finding the duplicated values from the dataframe
Duplicate_values = Data_Science_DataFrame[Data_Science_DataFrame.duplicated()]

print("The list of duplicated values in the dataframe are : ")
Duplicate_values

The list of duplicated values in the dataframe are :


# finding unique values of each column 

Work_year_column = Data_Science_DataFrame.work_year.unique()
Experience_level_column = Data_Science_DataFrame.experience_level.unique()
Employment_type_column = Data_Science_DataFrame.employment_type.unique()
Job_title_column = Data_Science_DataFrame.job_title.unique()
Remote_ratio_column = Data_Science_DataFrame.remote_ratio.unique()
Company_location = Data_Science_DataFrame.company_location.unique()
Company_size_column = Data_Science_DataFrame.company_size.unique()

#printing all the unique values from each column

print("1) The unique values from work year column are : \n",Work_year_column)
print()
print("2) The unique values from experience level column are : \n",Experience_level_column)
print()
print("3) The unique values from employment type column are : \n",Employment_type_column)
print()
print("4) The unique values from job title column are : \n",Job_title_column)
print()
print("5) The unique values from remote ratio column are : \n",Remote_ratio_column)
print()
print("6) The unique values from company location column are : \n",Company_location)
print()
print("7) The unique values from company size column are : \n",Company_size_column)

1) The unique values from work year column are : 
 [2023 2022 2020 2021]

2) The unique values from experience level column are : 
 ['SE' 'MI' 'EN' 'EX']

3) The unique values from employment type column are : 
 ['FT' 'CT' 'FL' 'PT']

4) The unique values from job title column are : 
 ['Principal Data Scientist' 'ML Engineer' 'Data Scientist'
 'Applied Scientist' 'Data Analyst' 'Data Modeler' 'Research Engineer'
 'Analytics Engineer' 'Business Intelligence Engineer'
 'Machine Learning Engineer' 'Data Strategist' 'Data Engineer'
 'Computer Vision Engineer' 'Data Quality Analyst'
 'Compliance Data Analyst' 'Data Architect'
 'Applied Machine Learning Engineer' 'AI Developer' 'Research Scientist'
 'Data Analytics Manager' 'Business Data Analyst' 'Applied Data Scientist'
 'Staff Data Analyst' 'ETL Engineer' 'Data DevOps Engineer' 'Head of Data'
 'Data Science Manager' 'Data Manager' 'Machine Learning Researcher'
 'Big Data Engineer' 'Data Specialist' 'Lead Data Analyst'
 'BI Data Engineer' 'Director of Data Science'
 'Machine Learning Scientist' 'MLOps Engineer' 'AI Scientist'
 'Autonomous Vehicle Technician' 'Applied Machine Learning Scientist'
 'Lead Data Scientist' 'Cloud Database Engineer' 'Financial Data Analyst'
 'Data Infrastructure Engineer' 'Software Data Engineer' 'AI Programmer'
 'Data Operations Engineer' 'BI Developer' 'Data Science Lead'
 'Deep Learning Researcher' 'BI Analyst' 'Data Science Consultant'
 'Data Analytics Specialist' 'Machine Learning Infrastructure Engineer'
 'BI Data Analyst' 'Head of Data Science' 'Insight Analyst'
 'Deep Learning Engineer' 'Machine Learning Software Engineer'
 'Big Data Architect' 'Product Data Analyst'
 'Computer Vision Software Engineer' 'Azure Data Engineer'
 'Marketing Data Engineer' 'Data Analytics Lead' 'Data Lead'
 'Data Science Engineer' 'Machine Learning Research Engineer'
 'NLP Engineer' 'Manager Data Management' 'Machine Learning Developer'
 '3D Computer Vision Researcher' 'Principal Machine Learning Engineer'
 'Data Analytics Engineer' 'Data Analytics Consultant'
 'Data Management Specialist' 'Data Science Tech Lead'
 'Data Scientist Lead' 'Cloud Data Engineer' 'Data Operations Analyst'
 'Marketing Data Analyst' 'Power BI Developer' 'Product Data Scientist'
 'Principal Data Architect' 'Machine Learning Manager'
 'Lead Machine Learning Engineer' 'ETL Developer' 'Cloud Data Architect'
 'Lead Data Engineer' 'Head of Machine Learning' 'Principal Data Analyst'
 'Principal Data Engineer' 'Staff Data Scientist' 'Finance Data Analyst']

5) The unique values from remote ratio column are : 
 [100   0  50]

6) The unique values from company location column are : 
 ['ES' 'US' 'CA' 'DE' 'GB' 'NG' 'IN' 'HK' 'NL' 'CH' 'CF' 'FR' 'FI' 'UA'
 'IE' 'IL' 'GH' 'CO' 'SG' 'AU' 'SE' 'SI' 'MX' 'BR' 'PT' 'RU' 'TH' 'HR'
 'VN' 'EE' 'AM' 'BA' 'KE' 'GR' 'MK' 'LV' 'RO' 'PK' 'IT' 'MA' 'PL' 'AL'
 'AR' 'LT' 'AS' 'CR' 'IR' 'BS' 'HU' 'AT' 'SK' 'CZ' 'TR' 'PR' 'DK' 'BO'
 'PH' 'BE' 'ID' 'EG' 'AE' 'LU' 'MY' 'HN' 'JP' 'DZ' 'IQ' 'CN' 'NZ' 'CL'
 'MD' 'MT']

7) The unique values from company size column are : 
 ['L' 'S' 'M']


#Replacing SE with Senior Level/Expert
Data_Science_DataFrame['experience_level'] = Data_Science_DataFrame['experience_level'].replace("SE", "Senior Level/Expert")
Data_Science_DataFrame


#Replacing MI with Medium Level/Intermediate
Data_Science_DataFrame['experience_level'] = Data_Science_DataFrame['experience_level'].replace("MI", "Medium Level/Intermediate")
Data_Science_DataFrame


#Replacing EN with Entry Level
Data_Science_DataFrame['experience_level'] = Data_Science_DataFrame['experience_level'].replace("EN", "Entry Level")
Data_Science_DataFrame


#Replacing EX with Executive Level
Data_Science_DataFrame['experience_level'] = Data_Science_DataFrame['experience_level'].replace("EX", "Executive Level")
Data_Science_DataFrame


# To calculate standard deviation, first we need to calculate sum, mean.

sum_of_salary = Data_Science_DataFrame['salary_in_usd'].sum() #To find the sum of the column salary.
print("The sum of the column salary is ",sum_of_salary)
print()

#Now to find the mean, we divide the sum of all the values in salary in usd column with the total number of values.

total_values = len(Data_Science_DataFrame['salary_in_usd'])
print("The total number of values in column salary is ",total_values)
print()

mean_of_salary = sum_of_salary / total_values
print("The mean is ",mean_of_salary)
print()


#We need to calculate the sum of square of the difference between the salary in usd and mean

squares_of_differences = (Data_Science_DataFrame['salary_in_usd'] - mean_of_salary) ** 2 # To find the square of differences between each value in salary and mean

sum_of_squares = squares_of_differences.sum() # finds the sum of all the squares

#Finally to calculate standard deviation

standard_deviation = (sum_of_squares / (total_values - 1)) ** 0.5 #This formula calculates the standard deviation of the taken column 'salary'.

print("The standard deviation of the column salary in usd is ",standard_deviation)

#Checking the answer with the function
Data_Science_DataFrame['salary_in_usd'].std()

The sum of the column salary is  516576814

The total number of values in column salary is  3755

The mean is  137570.38988015978

The standard deviation of the column salary in usd is  63055.6252782241

63055.625278224084


#To calculate skewness of the salary column

numerator1 = (((Data_Science_DataFrame['salary_in_usd'] - mean_of_salary) ** 3).sum())
denominator1 = ((total_values - 1) * (standard_deviation)**3)

skewness = numerator1 / denominator1

print("The skewness of the column salary is ",skewness)

The skewness of the column salary is  0.5361154662823647


#Assiging n to the total length of salary in usd column
n = total_values

#To calculate fourth central moment
fourth_moment = ((Data_Science_DataFrame['salary_in_usd'] - mean_of_salary) / standard_deviation)**4

#Using the formula of kurtosis
kurtosis = (n * (n+1) * fourth_moment.sum()) / ((n-1)*(n-2)*(n-3)) - (3*(n-1)**2) / ((n-2)*(n-3))

print("The kurtosis of the salary in usd column is ",kurtosis)

#Checking the answer with the function.
Data_Science_DataFrame['salary_in_usd'].kurt()

The kurtosis of the salary in usd column is  0.8340064594833612

0.8340064594833612


#This gives the total number of values in remote ratio column.

total_length = len(Data_Science_DataFrame['remote_ratio']) 

#It gives the total sum of all the values in remote ratio column.

sum_of_remote_ratio = Data_Science_DataFrame['remote_ratio'].sum() 

#It gives the mean of remote ratio column

mean_remote_ratio = sum_of_remote_ratio / total_length 


#Now to find the correlation of salary in usd and remote ratio column

numerator = ((Data_Science_DataFrame['salary_in_usd'] - mean_of_salary) * 
             (Data_Science_DataFrame['remote_ratio'] - mean_remote_ratio)).sum()

calc = (((Data_Science_DataFrame['salary_in_usd'] - mean_of_salary)**2).sum()) * (((Data_Science_DataFrame['remote_ratio'] - mean_remote_ratio)**2).sum())

denominator = calc**0.5

#Finally calculating correlation
correlation = numerator / denominator

print("The correlation of salary in usd and remote ratio column is  : ",correlation)

#Checking the answer with the function
Data_Science_DataFrame['salary_in_usd'].corr(Data_Science_DataFrame['remote_ratio'])

The correlation of salary in usd and remote ratio column is  :  -0.06417098519057553

-0.06417098519057557


#total length of the column work year
work_year_length = len(Data_Science_DataFrame['work_year']) 

# sum of all the values in this column
sum_work_year = Data_Science_DataFrame['work_year'].sum() 

#calculating mean 
mean_work_year = sum_work_year / work_year_length 

#For formula of correlation we need following :

numerator3 = ((Data_Science_DataFrame['salary_in_usd'] - mean_of_salary) * (Data_Science_DataFrame['work_year'] - mean_work_year)).sum()
calculation3 = (((Data_Science_DataFrame['salary_in_usd'] - mean_of_salary)**2).sum()) * (((Data_Science_DataFrame['work_year'] - mean_work_year)**2).sum())

denominator3 = calculation3 ** 0.5 #Denominator is in square root.

#Using the formula

correlation3 = numerator3 / denominator3

print("The correlation of salary in usd and work year column is  : ",correlation3)

#checking the answer with the function
Data_Science_DataFrame['salary_in_usd'].corr(Data_Science_DataFrame['work_year'])

The correlation of salary in usd and work year column is  :  0.22829002243287858

0.2282900224328786


#Assigning numerator5 for the upward calculations
numerator5 = ((Data_Science_DataFrame['work_year'] - mean_work_year) * 
              (Data_Science_DataFrame['remote_ratio'] - mean_remote_ratio)).sum()

#Assigning denominator5 for downward calculations
calculation5 = (((Data_Science_DataFrame['work_year'] - mean_work_year)**2).sum()) * (((Data_Science_DataFrame['remote_ratio'] - mean_remote_ratio)**2).sum())

denominator5 = calculation5 ** 0.5 #Denominator is in square root.

#Finally calculating correlation using the formula
correlation5 = numerator5 / denominator5


print("The correlation of work year and remote ratio column is  : ",correlation5)

#checking the answer with the function
Data_Science_DataFrame['work_year'].corr(Data_Science_DataFrame['remote_ratio'])

The correlation of work year and remote ratio column is  :  -0.23642984282567187

-0.23642984282567195


#Calculating the top 15 values according to number of employees.

Top_15_jobs = Data_Science_DataFrame['job_title'].value_counts().head(15)
print("The top 15 jobs are :")
Top_15_jobs

The top 15 jobs are :

job_title
Data Engineer                 1040
Data Scientist                 840
Data Analyst                   612
Machine Learning Engineer      289
Analytics Engineer             103
Data Architect                 101
Research Scientist              82
Data Science Manager            58
Applied Scientist               58
Research Engineer               37
ML Engineer                     34
Data Manager                    29
Machine Learning Scientist      26
Data Science Consultant         24
Data Analytics Manager          22
Name: count, dtype: int64


x = Top_15_jobs.index #Assigning the job titles in x
y = Top_15_jobs.values # Assgning the values in y


#Plotting the bar

plt.figure(figsize=(15,7)) # setting figure size
plt.bar(x,y,color='navy')
plt.xlabel('Job Titles', fontweight = 'bold', fontsize = 15) #labeling the x-axis and making it bold
plt.ylabel('Number of Employees', fontweight = 'bold', fontsize = 15) #labeling the y-axis and making it bold
plt.title('Top 15 jobs', fontweight = 'bold', fontsize = 15) # Assigning the title and making it bold
plt.xticks(rotation = 90) # rotating the x-axis labels
plt.grid(True) #showing grid

plt.show()


#calculating the top 5 jobs based on salary in usd
Highest_salary = Data_Science_DataFrame.groupby('job_title')['salary_in_usd'].max().sort_values(ascending = False).head(5) #head(5) gives the top 5 jobs

Highest_salary

job_title
Research Scientist                    450000
Data Analyst                          430967
AI Scientist                          423834
Applied Machine Learning Scientist    423000
Principal Data Scientist              416000
Name: salary_in_usd, dtype: int64


x = Highest_salary.index #Assigning the job titles in x
y = Highest_salary.values # Assgning the values in y

#Plotting the graph

plt.figure(figsize=(6,6)) #setting the figure size
plt.bar(x,y,width=0.5,color = ['red','gold','navy','firebrick','cyan']) 
plt.xlabel('Job Title', fontsize = 15, fontweight = 'bold') # labeling and setting the font for x label
plt.ylabel('Salaries in usd', fontsize = 15, fontweight = 'bold') # labeling and setting the font for y label
plt.title('Highest salaries job', fontsize = 15, fontweight = 'bold') # Assigning title and setting font for the title
plt.xticks(rotation = 90) # rotating the x-axis label.
plt.grid(True) #showing grid

plt.show()


#Calculating salary based on experiene level in descending form
experience = Data_Science_DataFrame.groupby('experience_level')['salary_in_usd'].max().sort_values(ascending = False)
experience

experience_level
Medium Level/Intermediate    450000
Senior Level/Expert          423834
Executive Level              416000
Entry Level                  300000
Name: salary_in_usd, dtype: int64


#Assigning the expereinece levels to x
x = experience.index

#Assigning the values to y
y = experience.values

#Plotting the bar

#Setting the figure size
plt.figure(figsize = (10,4))

#setting x-axis,y-axis,width and colour
plt.bar(x, y,width = 0.4,  color = ['mediumblue', 'blueviolet', 'peachpuff', 'darkred'])

#labeling the x-axis and setting the font
plt.xlabel('Experience Level', fontsize = 15, fontweight = 'bold')

#labeling the y-axis and setting the font
plt.ylabel('Salary in USD', fontsize = 15, fontweight = 'bold')

#Assigning the title and setting the font
plt.title('Salary based on experience level', fontsize = 15, fontweight = 'bold')
plt.xticks(rotation = 90) #rotating the x-axis label
plt.grid(True) #showing grid

plt.show()


#Setting figure size 
plt.figure(figsize=(15, 9))

#Setting the color, bins, edgecolor, linewidth of histogram
plt.hist(Data_Science_DataFrame['experience_level'],color='coral',bins=20,edgecolor = 'black', linewidth = 0.8)

#labeling the x-axis and setting font
plt.xlabel("Experience Level", fontsize = 15, fontweight = 'bold')

#labeling the y-axis and setting font
plt.ylabel("Number of employees", fontsize = 15, fontweight = 'bold')

#Assigning the title and setting the font
plt.title("Number of employees according to experience level",fontsize = 15, fontweight = 'bold')
plt.grid(True) #showing grid
plt.show()


#setting the figure size
plt.figure(figsize=(10,10))

#Box plotting
plt.boxplot(Data_Science_DataFrame['salary_in_usd'])

#labeling the y-axis and setting font
plt.ylabel('Salary', fontsize = 15, fontweight = 'bold')

#Assigning the title and setting the font
plt.title('Box plot of salary', fontsize = 15, fontweight = 'bold')
plt.grid(True)#showing grid
plt.show()

1 ) Write a python program to load data into pandas DataFrame.¶

2) Write a python program to remove unnecessary columns i.e., salary and salary currency.¶

3) Write a python program to remove the NaN missing values from updated dataframe.¶

4) Write a python program to check duplicates value in the dataframe.¶

5) Write a python program to see the unique values from all the columns in the dataframe.¶

6) Rename the experience level columns as below.¶

i) SE – Senior Level/Expert¶

ii) MI – Medium Level/Intermediate¶

iii) EN – Entry Level¶

iv) EX – Executive Level¶

7) Data Analysis¶

i) Write a Python program to show summary statistics of sum, mean, standard deviation, skewness, and kurtosis of any chosen variable.¶

Standard Deviation¶

Skewness¶

Kurtosis¶

ii) Write a Python program to calculate and show correlation of all variables.¶

a) Correlation of remote ratio and salary in usd column.¶

b) Correlation of salary in usd and work year column¶

c) Correlation of work year and remote ratio¶

9) Data Exploration¶

i) Write a python program to find out top 15 jobs. Make a bar graph of sales as well.¶

ii) Which job has the highest salaries? Illustrate with bar graph.¶

iii) Write a python program to find out salaries based on experience level. Illustrate it through bar graph.¶

iv) Write a Python program to show histogram and box plot of any chosen different variables. Use proper labels in the graph.¶

Histogram¶

Boxplot¶

	work_year	experience_level	employment_type	job_title	salary	salary_currency	salary_in_usd	employee_residence	remote_ratio	company_location	company_size
0	2023	SE	FT	Principal Data Scientist	80000	EUR	85847	ES	100	ES	L
1	2023	MI	CT	ML Engineer	30000	USD	30000	US	100	US	S
2	2023	MI	CT	ML Engineer	25500	USD	25500	US	100	US	S
3	2023	SE	FT	Data Scientist	175000	USD	175000	CA	100	CA	M
4	2023	SE	FT	Data Scientist	120000	USD	120000	CA	100	CA	M
5	2023	SE	FT	Applied Scientist	222200	USD	222200	US	0	US	L
6	2023	SE	FT	Applied Scientist	136000	USD	136000	US	0	US	L
7	2023	SE	FT	Data Scientist	219000	USD	219000	CA	0	CA	M
8	2023	SE	FT	Data Scientist	141000	USD	141000	CA	0	CA	M
9	2023	SE	FT	Data Scientist	147100	USD	147100	US	0	US	M
10	2023	SE	FT	Data Scientist	90700	USD	90700	US	0	US	M
11	2023	SE	FT	Data Analyst	130000	USD	130000	US	100	US	M
12	2023	SE	FT	Data Analyst	100000	USD	100000	US	100	US	M
13	2023	EN	FT	Applied Scientist	213660	USD	213660	US	0	US	L
14	2023	EN	FT	Applied Scientist	130760	USD	130760	US	0	US	L
15	2023	SE	FT	Data Modeler	147100	USD	147100	US	0	US	M
16	2023	SE	FT	Data Modeler	90700	USD	90700	US	0	US	M
17	2023	SE	FT	Data Scientist	170000	USD	170000	US	0	US	M
18	2023	SE	FT	Data Scientist	150000	USD	150000	US	0	US	M
19	2023	MI	FT	Data Analyst	150000	USD	150000	US	100	US	M
20	2023	MI	FT	Data Analyst	110000	USD	110000	US	100	US	M
21	2023	SE	FT	Research Engineer	275000	USD	275000	DE	0	DE	M
22	2023	SE	FT	Research Engineer	174000	USD	174000	DE	0	DE	M
23	2023	SE	FT	Analytics Engineer	230000	USD	230000	GB	100	GB	M
24	2023	SE	FT	Analytics Engineer	143200	USD	143200	GB	100	GB	M
25	2023	SE	FT	Business Intelligence Engineer	225000	USD	225000	US	0	US	M
26	2023	SE	FT	Business Intelligence Engineer	156400	USD	156400	US	0	US	M
27	2023	SE	FT	Machine Learning Engineer	200000	USD	200000	US	0	US	M
28	2023	SE	FT	Machine Learning Engineer	130000	USD	130000	US	0	US	M
29	2023	SE	FT	Data Strategist	90000	USD	90000	CA	0	CA	M
30	2023	SE	FT	Data Strategist	72000	USD	72000	CA	0	CA	M
31	2023	SE	FT	Data Engineer	253200	USD	253200	US	0	US	M
32	2023	SE	FT	Data Engineer	90700	USD	90700	US	0	US	M
33	2023	SE	FT	Computer Vision Engineer	342810	USD	342810	US	0	US	M
34	2023	SE	FT	Computer Vision Engineer	184590	USD	184590	US	0	US	M
35	2023	MI	FT	Data Engineer	162500	USD	162500	US	0	US	M
36	2023	MI	FT	Data Engineer	130000	USD	130000	US	0	US	M
37	2023	MI	FT	Data Analyst	105380	USD	105380	US	0	US	M
38	2023	MI	FT	Data Analyst	64500	USD	64500	US	0	US	M
39	2023	EN	FT	Data Quality Analyst	100000	USD	100000	NG	100	NG	L

	work_year	experience_level	employment_type	job_title	salary_in_usd	employee_residence	remote_ratio	company_location	company_size
0	2023	SE	FT	Principal Data Scientist	85847	ES	100	ES	L
1	2023	MI	CT	ML Engineer	30000	US	100	US	S
2	2023	MI	CT	ML Engineer	25500	US	100	US	S
3	2023	SE	FT	Data Scientist	175000	CA	100	CA	M
4	2023	SE	FT	Data Scientist	120000	CA	100	CA	M
5	2023	SE	FT	Applied Scientist	222200	US	0	US	L
6	2023	SE	FT	Applied Scientist	136000	US	0	US	L
7	2023	SE	FT	Data Scientist	219000	CA	0	CA	M
8	2023	SE	FT	Data Scientist	141000	CA	0	CA	M
9	2023	SE	FT	Data Scientist	147100	US	0	US	M
10	2023	SE	FT	Data Scientist	90700	US	0	US	M
11	2023	SE	FT	Data Analyst	130000	US	100	US	M
12	2023	SE	FT	Data Analyst	100000	US	100	US	M
13	2023	EN	FT	Applied Scientist	213660	US	0	US	L
14	2023	EN	FT	Applied Scientist	130760	US	0	US	L
15	2023	SE	FT	Data Modeler	147100	US	0	US	M
16	2023	SE	FT	Data Modeler	90700	US	0	US	M
17	2023	SE	FT	Data Scientist	170000	US	0	US	M
18	2023	SE	FT	Data Scientist	150000	US	0	US	M
19	2023	MI	FT	Data Analyst	150000	US	100	US	M
20	2023	MI	FT	Data Analyst	110000	US	100	US	M
21	2023	SE	FT	Research Engineer	275000	DE	0	DE	M
22	2023	SE	FT	Research Engineer	174000	DE	0	DE	M
23	2023	SE	FT	Analytics Engineer	230000	GB	100	GB	M
24	2023	SE	FT	Analytics Engineer	143200	GB	100	GB	M
25	2023	SE	FT	Business Intelligence Engineer	225000	US	0	US	M
26	2023	SE	FT	Business Intelligence Engineer	156400	US	0	US	M
27	2023	SE	FT	Machine Learning Engineer	200000	US	0	US	M
28	2023	SE	FT	Machine Learning Engineer	130000	US	0	US	M
29	2023	SE	FT	Data Strategist	90000	CA	0	CA	M
30	2023	SE	FT	Data Strategist	72000	CA	0	CA	M
31	2023	SE	FT	Data Engineer	253200	US	0	US	M
32	2023	SE	FT	Data Engineer	90700	US	0	US	M
33	2023	SE	FT	Computer Vision Engineer	342810	US	0	US	M
34	2023	SE	FT	Computer Vision Engineer	184590	US	0	US	M
35	2023	MI	FT	Data Engineer	162500	US	0	US	M
36	2023	MI	FT	Data Engineer	130000	US	0	US	M
37	2023	MI	FT	Data Analyst	105380	US	0	US	M
38	2023	MI	FT	Data Analyst	64500	US	0	US	M
39	2023	EN	FT	Data Quality Analyst	100000	NG	100	NG	L

	work_year	experience_level	employment_type	job_title	salary_in_usd	employee_residence	remote_ratio	company_location	company_size
0	2023	SE	FT	Principal Data Scientist	85847	ES	100	ES	L
1	2023	MI	CT	ML Engineer	30000	US	100	US	S
2	2023	MI	CT	ML Engineer	25500	US	100	US	S
3	2023	SE	FT	Data Scientist	175000	CA	100	CA	M
4	2023	SE	FT	Data Scientist	120000	CA	100	CA	M
5	2023	SE	FT	Applied Scientist	222200	US	0	US	L
6	2023	SE	FT	Applied Scientist	136000	US	0	US	L
7	2023	SE	FT	Data Scientist	219000	CA	0	CA	M
8	2023	SE	FT	Data Scientist	141000	CA	0	CA	M
9	2023	SE	FT	Data Scientist	147100	US	0	US	M
10	2023	SE	FT	Data Scientist	90700	US	0	US	M
11	2023	SE	FT	Data Analyst	130000	US	100	US	M
12	2023	SE	FT	Data Analyst	100000	US	100	US	M
13	2023	EN	FT	Applied Scientist	213660	US	0	US	L
14	2023	EN	FT	Applied Scientist	130760	US	0	US	L
15	2023	SE	FT	Data Modeler	147100	US	0	US	M
16	2023	SE	FT	Data Modeler	90700	US	0	US	M
17	2023	SE	FT	Data Scientist	170000	US	0	US	M
18	2023	SE	FT	Data Scientist	150000	US	0	US	M
19	2023	MI	FT	Data Analyst	150000	US	100	US	M
20	2023	MI	FT	Data Analyst	110000	US	100	US	M
21	2023	SE	FT	Research Engineer	275000	DE	0	DE	M
22	2023	SE	FT	Research Engineer	174000	DE	0	DE	M
23	2023	SE	FT	Analytics Engineer	230000	GB	100	GB	M
24	2023	SE	FT	Analytics Engineer	143200	GB	100	GB	M
25	2023	SE	FT	Business Intelligence Engineer	225000	US	0	US	M
26	2023	SE	FT	Business Intelligence Engineer	156400	US	0	US	M
27	2023	SE	FT	Machine Learning Engineer	200000	US	0	US	M
28	2023	SE	FT	Machine Learning Engineer	130000	US	0	US	M
29	2023	SE	FT	Data Strategist	90000	CA	0	CA	M
30	2023	SE	FT	Data Strategist	72000	CA	0	CA	M
31	2023	SE	FT	Data Engineer	253200	US	0	US	M
32	2023	SE	FT	Data Engineer	90700	US	0	US	M
33	2023	SE	FT	Computer Vision Engineer	342810	US	0	US	M
34	2023	SE	FT	Computer Vision Engineer	184590	US	0	US	M
35	2023	MI	FT	Data Engineer	162500	US	0	US	M
36	2023	MI	FT	Data Engineer	130000	US	0	US	M
37	2023	MI	FT	Data Analyst	105380	US	0	US	M
38	2023	MI	FT	Data Analyst	64500	US	0	US	M
39	2023	EN	FT	Data Quality Analyst	100000	NG	100	NG	L

	work_year	experience_level	employment_type	job_title	salary_in_usd	employee_residence	remote_ratio	company_location	company_size
115	2023	SE	FT	Data Scientist	150000	US	0	US	M
123	2023	SE	FT	Analytics Engineer	289800	US	0	US	M
153	2023	MI	FT	Data Engineer	100000	US	100	US	M
154	2023	MI	FT	Data Engineer	70000	US	100	US	M
160	2023	SE	FT	Data Engineer	115000	US	0	US	M
163	2023	SE	FT	Applied Scientist	309400	US	0	US	L
164	2023	SE	FT	Applied Scientist	159100	US	0	US	L
167	2023	MI	FT	Data Engineer	162500	US	0	US	M
168	2023	MI	FT	Data Engineer	130000	US	0	US	M
171	2023	SE	FT	Data Engineer	145000	US	0	US	M
173	2023	SE	FT	Applied Scientist	222200	US	0	US	L
174	2023	SE	FT	Applied Scientist	136000	US	0	US	L
189	2023	SE	FT	Data Engineer	140000	US	0	US	M
195	2023	SE	FT	Data Scientist	170000	US	0	US	M
201	2023	SE	FT	Data Architect	115000	US	100	US	M
206	2023	MI	FT	Data Engineer	130000	US	0	US	M
223	2023	SE	FT	Data Engineer	81500	US	0	US	M
224	2023	SE	FT	Data Engineer	145000	US	0	US	M
225	2023	SE	FT	Data Engineer	115000	US	0	US	M
226	2023	SE	FT	Data Engineer	185000	US	0	US	M
227	2023	SE	FT	Data Engineer	140000	US	0	US	M
235	2023	SE	FT	Data Engineer	145000	US	0	US	M
250	2023	EN	FT	Data Engineer	85000	US	0	US	M
251	2023	EN	FT	Data Engineer	65000	US	0	US	M
258	2023	SE	FT	Data Engineer	220000	US	100	US	M
264	2023	SE	FT	Data Scientist	48289	ES	0	ES	M
265	2023	SE	FT	Data Scientist	38631	ES	0	ES	M
273	2023	SE	FT	Applied Scientist	136000	US	0	US	L
276	2023	SE	FT	Applied Scientist	260000	US	0	US	L
277	2023	SE	FT	Applied Scientist	136000	US	0	US	L
284	2023	SE	FT	Data Scientist	195000	US	0	US	M
285	2023	SE	FT	Data Scientist	160000	US	0	US	M
288	2023	SE	FT	Data Scientist	170000	US	0	US	M
290	2023	SE	FT	Data Scientist	175000	US	100	US	M
291	2023	SE	FT	Data Scientist	145000	US	100	US	M
294	2023	SE	FT	Applied Scientist	222200	US	0	US	L
295	2023	SE	FT	Applied Scientist	136000	US	0	US	L
302	2023	SE	FT	Data Scientist	175000	US	100	US	M
309	2023	SE	FT	Data Analyst	75000	US	100	US	M
330	2023	SE	FT	Data Engineer	145000	US	0	US	M